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Integrative network analysis for survival-associated gene-gene interactions across multiple genomic profiles in ovarian cancer.

ABSTRACT: Recent advances in high-throughput technology and the emergence of large-scale genomic datasets have enabled detection of genomic features that affect clinical outcomes. Although many previous computational studies have analysed the effect of each single gene or the additive effects of multiple genes on the clinical outcome, less attention has been devoted to the identification of gene-gene interactions of general type that are associated with the clinical outcome. Moreover, the integration of information from multiple molecular profiles adds another challenge to this problem. Recently, network-based approaches have gained huge popularity. However, previous network construction methods have been more concerned with the relationship between features only, rather than the effect of feature interactions on clinical outcome.We propose a mutual information-based integrative network analysis framework (MINA) that identifies gene pairs associated with clinical outcome and systematically analyses the resulting networks over multiple genomic profiles. We implement an efficient non-parametric testing scheme that ensures the significance of detected gene interactions. We develop a tool named MINA that automates the proposed analysis scheme of identifying outcome-associated gene interactions and generating various networks from those interacting pairs for downstream analysis.We demonstrate the proposed framework using real data from ovarian cancer patients in The Cancer Genome Atlas (TCGA). Statistically significant gene pairs associated with survival were identified from multiple genomic profiles, which include many individual genes that have weak or no effect on survival. Moreover, we also show that integrated networks, constructed by merging networks from multiple genomic profiles, demonstrate better topological properties and biological significance than individual networks.We have developed a simple but powerful analysis tool that is able to detect gene-gene interactions associated with clinical outcome on multiple genomic profiles. By being network-based, our approach provides a better insight into the underlying gene-gene interaction mechanisms that affect the clinical outcome of cancer patients.

SUBMITTER: Jeong HH

PROVIDER: S-EPMC4491426 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Integrative network analysis for survival-associated gene-gene interactions across multiple genomic profiles in ovarian cancer.

Jeong Hyun-Hwan HH Leem Sangseob S Wee Kyubum K Sohn Kyung-Ah KA

Journal of ovarian research 20150703

<h4>Background</h4>Recent advances in high-throughput technology and the emergence of large-scale genomic datasets have enabled detection of genomic features that affect clinical outcomes. Although many previous computational studies have analysed the effect of each single gene or the additive effects of multiple genes on the clinical outcome, less attention has been devoted to the identification of gene-gene interactions of general type that are associated with the clinical outcome. Moreover, t ...[more]

PMID: 26138921

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Project description:BackgroundThe increasing availability of multiple types of genomic profiles measured from the same cancer patients has provided numerous opportunities for investigating genomic mechanisms underlying cancer. In particular, association studies of gene expression traits with respect to multi-layered genomic features are highly useful for uncovering the underlying mechanism. Conventional correlation-based association tests are limited because they are prone to revealing indirect associations. Moreover, integration of multiple types of genomic features raises another challenge.MethodsIn this study, we propose a new framework for association studies called integrative regression network that identifies genomic associations on multiple high-dimensional genomic profiles by taking into account the associations between as well as within profiles. We employed high-dimensional regression techniques to first identify the associations between different genomic profiles. Based on the resulting regression coefficients, a regression network was constructed within each profile. For example, two methylation features having similar regression coefficients with respect to a number of gene expression traits are likely to be involved in the same biological process and therefore we define an edge between two methylation features in the regression network. To extract more reliable associations, multiple sparse structured regression techniques were applied and the resulting multiple networks were merged as the integrative regression network using a similarity network fusion technique.ResultsExperiments were carried out using four different sparse structured regression methods on five cancer types from TCGA. The advantages and disadvantages of each regression method were also explored. We find there was large inconsistency in the results from different regression methods, which supports the need to extract the proposed integrative regression network from multiple complimentary regression techniques. Fusing multiple regression networks by using similarity measurements led to the identification of significant gene pairs and a resulting network with better topological properties.ConclusionsWe developed and validated the integrative regression network scheme on multi-layered genomic profiles from TCGA. Our method facilitates identification of the strong signals as well as weaker signals by fusing information from different regression techniques. It could be extended to integrate results obtained from different cancer types as well.

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Dataset Information

Integrative network analysis for survival-associated gene-gene interactions across multiple genomic profiles in ovarian cancer.

Publications

Integrative network analysis for survival-associated gene-gene interactions across multiple genomic profiles in ovarian cancer.

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